Predicting missing items

ABSTRACT

In some embodiments, there is provided a system. The system may include at least one data processor and at least one memory storing instructions which, when executed by the at least one data processor, cause the apparatus to at least: determine, for a received document including at least one item, that the received document likely includes at least one missing item, the determination based on at least a machine learning model and the at least one item; and provide an indication of the at least one missing item. Related systems and articles of manufacture are also provided.

TECHNICAL FIELD

The subject matter described herein relates generally to machinelearning.

BACKGROUND

Health care systems are specific to each country and change rapidly toadapt to new needs and societal-specific structures. Daily hospitalcosts are very different from country to country. Hospital surgeries area driver of U.S. healthcare spending inflation as well as for othercountries. In some regions, hospital reimbursement models are changingfrom a lump payment to individual billing. Health care systems are facedwith extreme pressure to reduce cost while providing the same if nothigher quality of care.

SUMMARY

Systems, methods, and articles of manufacture, including computerprogram products, are provided for detecting and/or recommending missingitems.

In some embodiments, there is provided a system. The system may includeat least one data processor and at least one memory storing instructionswhich, when executed by the at least one data processor, cause theapparatus to at least: determine, for a received document including atleast one item, that the received document likely includes at least onemissing item, the determination based on at least a machine learningmodel and the at least one item; and provide an indication of the atleast one missing item.

In some variations, one or more of the features disclosed hereinincluding the following features can optionally be included in anyfeasible combination. The received document may include an invoice. Theat least one item may include a hospital billing code. Themachine-learning model may include a co-occurrence model. Theco-occurrence model may include a matrix including a valuerepresentative of a likelihood that pairs of items are likely to beincluded in the received document, the pairs of items including the atleast one item and the at least one missing item. The co-occurrencemodel may include a co-occurrence matrix including a valuerepresentative of a likelihood that pairs of items are likely to beincluded in the received document. In some implementations, theco-occurrence model advantageously provides missing items from invoices.A recommendation item corresponding to the at least one missing item maybe provided (e.g., for display at a user interface). A value indicativeof a confidence that the at least one missing item is missing from thereceived document may be provided (e.g., for display at a userinterface). A value indicative of a confidence that the at least onemissing item is missing from the received document may be provided(e.g., for display at a user interface). The machine-learning model maybe trained based on at least a set of reference documents that include aset of verified items. The machine-learning model may be generated basedon at least a statistical technique, a neural network, a patternrecognizer, a clustering algorithm, a rule-based engine, a prioriinformation, a convolutional neural network, and/or a recurrent neuralnetwork.

Implementations of the current subject matter can include methodsconsistent with the descriptions provided herein as well as articlesthat comprise a tangibly embodied machine-readable medium operable tocause one or more machines (e.g., computers, etc.) to result inoperations implementing one or more of the described features.Similarly, computer systems are also described that may include one ormore processors and one or more memories coupled to the one or moreprocessors. A memory, which can include a non-transitorycomputer-readable or machine-readable storage medium, may include,encode, store, or the like one or more programs that cause one or moreprocessors to perform one or more of the operations described herein.Computer implemented methods consistent with one or more implementationsof the current subject matter can be implemented by one or more dataprocessors residing in a single computing system or multiple computingsystems. Such multiple computing systems can be connected and canexchange data and/or commands or other instructions or the like via oneor more connections, including, for example, to a connection over anetwork (e.g. the Internet, a wireless wide area network, a local areanetwork, a wide area network, a wired network, or the like), via adirect connection between one or more of the multiple computing systems,etc.

The details of one or more variations of the subject matter describedherein are set forth in the accompanying drawings and the descriptionbelow. Other features and advantages of the subject matter describedherein will be apparent from the description and drawings, and from theclaims. While certain features of the currently disclosed subject matterare described for illustrative purposes in relation to a machinelearning based plug-in for accessing a cloud-based analytics engine, itshould be readily understood that such features are not intended to belimiting. The claims that follow this disclosure are intended to definethe scope of the protected subject matter.

DESCRIPTION OF DRAWINGS

The accompanying drawings, which are incorporated in and constitute apart of this specification, show certain aspects of the subject matterdisclosed herein and, together with the description, help explain someof the principles associated with the disclosed implementations. In thedrawings,

FIG. 1 depicts an example of a system, in accordance with some exampleembodiments;

FIG. 2 depicts an example of the system of FIG. 1 during a trainingphase, in accordance with some example embodiments;

FIG. 3 depicts an example of an invoice-billing item table, inaccordance with some example embodiments;

FIGS. 4A, B, and C depict examples of observed-relations tables, inaccordance with some embodiments;

FIGS. 5A, B, C, and D depict an example of a ML model, in accordancewith some embodiments;

FIG. 6 depicts another example of a system, in accordance with someexample embodiments;

FIG. 7 depicts an example of a process for detecting missing items, inaccordance with some embodiments; and

FIG. 8 depicts a block diagram illustrating a computing system, inaccordance with some example embodiments.

When practical, similar reference numbers denote similar structures,features, or elements.

DETAILED DESCRIPTION

As noted, health care systems face challenges with respect to providingservices more efficiently. To that end, identifying unbilled line itemsin invoices may be a factor in enabling more efficient cost recovery bydata processing systems associated with health care systems.

To illustrate further, a patient may receive care and that care may beinvoiced and submitted electronically for payment. For example, theinvoice may be for a given service, such as stich a patient's forearm.For that service, there may one or more items associated with theservice. To illustrate using the stitches example, the invoice mayinclude a code for the diagnosis (e.g., patient has forearm wound andneeds stitches on forearm) and one or more codes for the itemsassociated with the stitches. These items may be in the form of codes,such as hospital billing codes, although other types of codes, symbols,textual descriptions, or indicators may be used. In the stiches example,the items may include a first code for sewing up the wound, a secondcode for an antiseptic, a third code for an antibiotic, and a fourthcode for gauze. In this example, the application of the stitch willlikely be associated with a specific set, group of items, such ashospital billing codes. All too often, when a given patient undergoes atreatment, the invoice may not include all of the billing codes for theservices rendered. As such, the health care system is not properlyrecovering costs, which may ultimately result in inefficiencies in thesystem that drive up the cost of healthcare. There is thus a need toprovide better automated processing of documents, such as invoices,hospital invoices, and the like, to detect missing line items.

In some example embodiments, there is provided a system includingmachine-learning to detect one or more missing items in a document suchas an invoice. In some example embodiments, the system may detectmissing billing codes, such as hospital billing codes, from invoicessuch as hospital invoices.

FIG. 1 depicts an example of a system 100, in accordance with someexample embodiments. The system 100 may include a machine-learning (ML)model 110, which has been trained to detect one or more missing items ina document such as an invoice. This trained ML model may receive adocument, such as document 1 102A, and detect whether document 1 ismissing one or more items. As used herein, the ML model refers to amodel formed based on machine learning to detect one or more missingitems and/or recommend a missing item. The ML model may be generatedbased on statistical techniques, neural networks, pattern recognizers,clustering algorithms, rule-based engines (based on a priori knowledge),convolutional neural networks, recurrent neural network, and/or acombination of one or more of these technologies.

For example, the document 1 102A may correspond to an invoice, such as ahospital invoice. This document 1 may include one or more items 104A, B,and Z. These items may represent codes, such as hospital billing codes.Referring to the previous example of stitching up a wound, the item A104A may indicate sewing up the wound, item B 104B may indicate theantiseptic, and the item 104Z may indicate gauze. The ML model 110 mayreceive the document 1 including the items 104A, B, and Z and determinewhether there are any missing items. In this example, the ML model maydetect that item C 102D (which may indicate antibiotic) is missing fromthe document 102A. In this way, the error in the document 1 may bedetected and/or flagged for further processing, such as adding themissing item 102C, auditing the document 102A, and the like.

Although some of the examples refer to the documents, such as document102A, the documents may represent an invoice or a portion of an invoice.Moreover, the document may be structured in other forms such as an entryin a database. For example, the document including the items may beinstantiated as a row or a column of a database management system, andthe document may be provided as an input to the ML model 110.

The ML model 110 may be trained to detect whether there are any missingitems. In some example embodiments, the ML model 110 may comprise aco-occurrence model. This co-occurrence model may be trained to learnthe likelihood that a group (or set, for example) of items are likely tofound together. In other words, the co-occurrence model is trained todetermine whether, given a first item, a second item is likely to bepresent in a group of items. The ML model and/or the co-occurrence modelmay be generated based on a prior information, statistical, and/orrules. In the example of FIG. 1, the ML model 110 may detect that, givenitems 104A, 104B, and/or 104C, the item C 102D (which may indicate theantibiotic) is missing from document 102A. In this way, the ML model maydetect the error in document 1 102A and may flag document 1 for furtherprocessing, such as adding the missing item 102C and the like.

The example shown at FIG. 1 also depicts document 2 105A having items104A, 104E, 104C, and 104D. This document 2 may be received by the MLmodel 110. But in this example, the ML model determines that there areno missing items in document 2 105A, so the ML model outputs anindication that there are no missing items (see, e.g., okay 199). Inother words, the set of items 104A, 104E, 104C, and 104D is likely to bea complete set of items.

Given the complexity of hospital billing codes and the complexity of thecombinations of allowable combinations of authorized billing codes, ahuman cannot reliably detect the missing items from the large quantityof hospital invoices, so the system 100 provides a novel way to processthese electronic documents to detect missing items, such as billingcodes.

FIG. 2 depicts a training phase for the ML model 110, in accordance withsome example embodiments. In the example of FIG. 2, the ML model 110 maybe provided with a plurality of reference documents 202A-N. Thesereference documents may represent invoices confirmed or somehow checkedto include a complete or proper set of items. For example, the referencedocument 202A may represent a reference invoice for the stichesprocedure noted above, and this reference document 202A has beenconfirmed to include the complete set of line items 204A, B, D, and C.Each of the reference documents 202A-202N may also represent referenceinvoices for the same or a different procedure (or, e.g., service). Eachof the reference documents 202A-202N may include a corresponding set ofitems that have been checked to confirm the grouped line items arelikely a complete, proper group of line items.

To train the ML model 110, the reference documents 202A-N are providedto the ML model to enable the ML model to learn the co-occurrencegrouping of items found on the reference documents. In the case ofimplementing the ML model as a co-occurrence model, the ML model maygenerate a matrix, such as a co-occurrence matrix. This matrix mayindicate the likelihood that given a first item, a second item should bepreset. This likelihood may be in terms of a score or percentage. The MLmodel may also be trained to learn clusters or groups of items. Whenthis is the case, the ML training with the reference documents enablesthe ML model to form a cluster in n-dimensional space of the line itemsthat are likely found together on a document such as an invoice. Inother words, the training enables the ML model to determine thelikelihood that a group of items should co-occur on the same invoice fora particular service, such as the stitch service associated withdocument 1 102A noted above. In this way, if a new document is receivedfor processing after the training phase of the ML model, the trained MLmodel 110 may provide an indication of whether then new document ismissing an item. The indication may be in the form of a likelihood thatan item is missing from the document. Alternatively or additionally, theindication may include the identity of the missing line item (orcandidate missing items) as well. For example, the indication mayindicate an invoice is missing one or more hospital billing codes.Alternatively or additionally, the indication may state the identitiesof the missing items such as item C 102D as shown at FIG. 1.

Although the ML model 110 may be implemented as a co-occurrence model,the ML model may be implemented using other types of ML technologies,such as a convolutional neural network, recurrent neural network, and/orother type of ML technology. The following provides an exampleimplementation of the ML model 110 as a co-occurrence model.

The ML model 110 may be implemented as a matrix. For example, the MLmodel (which detects and/or recommends a missing item) may beimplemented in a matrix form. For each pairwise combination of items,the matrix may include a likelihood that the pair of items are likely tobe found together in a given document. The matrix may comprise a squarematrix and/or may be symmetric diagonal matrix. The following providesan example of how to generate the ML model's matrix, although the matrixform may be realized in other structures such as a table, vectors, andother ways to provide the noted ML model that detects and/or recommendsmissing items.

To generate the ML model's matrix, the reference documents (see, e.g.,202A-N) may be used. For example, the reference documents 202A-N may beprocessed into a matrix or table, an example of which is shown at FIG.3. FIG. 3 depicts an example of the table 300, which in this example isreferred to as the invoice-billing item table 300. This table includesthe possible combinations of items, such as billing items, associatedwith each of the reference documents, such as reference invoices. And,table 300 illustrates an example including 5 reference documents, whichare reference invoices 1-5 in this example.

To generate invoice-billing item table 300, the reference invoices(which are form the training set of invoices) may be processed toidentify the allowed set of billing items in the reference invoices. Inthe example of FIG. 3, the set of allowed billing items 305 may beinserted into the header 305 of the table 300 as HPV, Tdap, Influenza,Preventive Service, Admin first, and Admin second. In some embodiments,the allowed billing items may be billing codes which are authorized forreimbursement, while items that are not allowed (e.g., not authorizedfor reimbursement) may be excluded from the list of allowed items andthus not listed at the header 305.

The invoice-billing item table 300 may be populated with the item datafrom each of the reference invoices. For the first reference invoice “1”for example, the first row of the table 300 may be populated with anindication for each of the billing codes found in the reference invoice1, which in this example includes A (HPV), B (Preventive Service), and C(Admin first). Likewise, for the second reference invoice “2” forexample, the second row of the table 300 may be populated with anindication for each of the billing codes found in the reference invoice2, which in this example includes D (Tdap), E (Preventive Service), F(Admin First), and G (Admin Second), and so forth for each invoice andcorresponding row of table 3. Although the indications in this exampleare represented by the values A, B, C, so forth, in someimplementations, the value “1” is used to indicate the presence of thebilling code, and a “0” is used to show it is not present. Referringagain to the reference invoice 1, the row would be as follows: 1, 0, 0,1, 1, and 0.

FIG. 4A depicts an example of an observed-relations table 400. Theobserved relations table 400 is derived from the invoice-billing itemtable 300. Specifically, the observed relations table 400 includes 5columns and n rows, wherein n is the number of all observed item-itemco-occurrences. For example, the item-item co-occurrence refers to thepresence of a specific pair of billing items on the same referenceinvoice. To illustrate further with reference invoice 1 at FIG. 3, theHPV item co-occurs with Preventive Service item and Admin First item. Inthis example, the first pair A (for HPV) and B (for Preventive Service)is shown at 402A, while the second pair A (for HPV) and B (for AdminFirst) is shown at 402B. The co-occurrence between Preventive Serviceand Admin First is also added to table 400 at the third line 402C tocomplete the contribution of the reference invoice 1 to the observedrelations table 400 shown at FIG. 4B. The remaining invoices may beprocessed and the observed relations may be added to the observedrelations table 400 as shown at FIG. 4C. In the example of FIG. 4C,there are 28 relations observed from a dataset of 5 reference invoices.

As noted, the ML model 110 may be in the form of a squared, diagonallysymmetrical matrix of billing items both on columns and on rows. FIG. 5Adepicts an example this matrix 500, which may be initially generated toinclude rows and columns corresponding to the types of billing items (orcodes). Referring to the example of FIG. 5A, the table 500 consists of asymmetric squared matrix having n columns and n rows, whereby n is thenumber of distinct types of billing items (in this example 6 itemscorresponding to HPV, Tdap, Influenza, Preventive Service, Admin first,and Admin second). FIG. 5A depicts the initialized count values set tozero, but the count values (at each cell) are updated from “0” toinclude the counts of co-occurrence between the pairs of billing itemscorresponding to the row and column. For example, the count value 506may be updated to the quantity of occurrences of HPV and Influenza ineach of the reference invoice documents.

For each row of the observed relations table 400 (FIG. 4C), thecorresponding cell of the matrix 500 is updated with the counts ofco-occurrences for each of the pairs of items (e.g., the paircorresponding to the row and column). Referring to the previous example,the processing of the first 3 rows of the observed relations table 400will result in the cell content shown in FIG. 5B. FIG. 5C depicts thecount based processing of all of the rows of FIG. 4C. Based on FIG. 5Cfirst row 522A, there is 0 co-occurrence of HPV and HPV in the referenceinvoices 1-5 (FIG. 4C), 1 co-occurrence of HPV and Tdap, 1 co-occurrenceof HPV and Influenza, 3 co-occurrences of HPV and Preventive Service, 3co-occurrences of HPV and Admin First, and 1 co-occurrences of HPV andAdmin Second. And based on FIG. 5C second row 522B, there is 1co-occurrence of Tdap and HPV, 2 co-occurrences of Tdap and PreventiveService, 2 co-occurrences of Tdap and Admin First, and 2 co-occurrencesof Tdap and Admin Second, and so forth through the rows at table 500 atFIG. 5C.

In some implementations, the matrix 500, the matrix may be normalized.For example, the count values may be normalized to a value between 0 and1 or to a percentage. FIG. 5D depicts the matrix of FIG. 5C normalizedinto percentages. For example, the count in a given cell may benormalized by dividing the cell's count with the total number ofinvoices processed, and then multiplying with 100. In the currentexample with 5 invoices, each count is divided by 5, and then multipliedby 100. Referring to count 1 at 509A (FIG. 5C), it is normalized to 20509B at FIG. 5D (e.g., 1 divided by 20 equals 0.20; 0.20 multiplied by100). The matrix 500 at FIG. 5C may be considered a co-occurrencematrix.

The matrix 500 at FIG. 5C or 4D may be considered a co-occurrencematrix, which may be used as the ML model 110.

FIG. 6 depicts the ML model 110 implemented to include the co-occurrencematrix noted above with respect to FIG. 5D. In the example of FIG. 6,the ML model 110 has been trained based on the reference invoices notedabove. When a new invoice 610 is received by the ML model 110, the MLmodel is used to determine the likelihood (e.g., scores, strengths,confidence values) that other items should be present in invoice 610. Toillustrate, invoice 610 includes HPV and Preventive Service. As such,there is 80% chance that given HPV and Preventive Service, Admin Firstshould be included in the group as well. There is only a 30% chance thatTdap should be included in the grouping of items, a 10% likelihood thatInfluenza should be part of the grouping, and a 40% likelihood thatAdmin Second should be included in the grouping. In some embodiments,the items Tdap, Influenza, and Admin Second may be reported as possiblemissing items (along with their strengths or scores). In someembodiments, a threshold value may be used to determine whether toinclude the item. For example, the threshold may be set at 51%, in whichcase only the Preventive Service is included in the grouping the items.

Referring to FIG. 6 at row 602, HPV and Preventive Service are includedin the received document 610, so there is no likelihood determinationprovided by the model 110 as shown by “given.” But the likelihood thatTdap is present given HPV is 20% and the likelihood that Tdap is presentgiven Preventive Service (PS) is 40% (see also contents of table 500 atFIG. 5D), so the ML model 110 may combine (e.g., average) this toprovide a 30% likelihood 604 that Tdap is present given that HPV and PSare found in the received document. Likewise, the likelihood thatInfluenza is present given HPV is 0% and the likelihood that Influenzais present given PS is 20% (see also contents of table 500 at FIG. 5D),so the ML model 110 may combine this to provide a 10% likelihood thatInfluenza is present given that HPV and PS are found in the receiveddocument. And, the likelihood that Admin First is present given HPV is60% and the likelihood that Admin First is present given PS is 100% (seealso contents of table 500 at FIG. 5D), so the ML model 110 may combinethis to provide a 80% likelihood that Admin First is present given thatHPV and PS are found in the received document. Furthermore, thelikelihood that Admin Second is present given HPV is 20% and thelikelihood that Admin Second is present given PS is 60% (see alsocontents of table 500 at FIG. 5D), so the ML model 110 may combine thisto provide a 40% likelihood that Admin Second is present given that HPVand PS are found in the received document. This example shows thepairwise likelihood between pairs of items included in the MLco-occurrence model 110.

FIG. 7 depicts an example of a process flow 700, in accordance with someexample embodiments.

At 702, a determination may be made regarding whether a receiveddocument likely includes at least one missing item. Referring also toFIG. 6 for example, the ML model 110 may receive a document 610. The MLmodel 110 (which has been trained) may determine that the receiveddocument likely includes at least one missing item. For example, the MLmodel may determine that Admin First is likely missing, and thisdetermination may be based on the item(s) included in the receiveddocument. As noted, the presence of HPV and Preventive Service billingcodes in document 610 may provide a likelihood that Admin First is alsomissing. In this example, the likelihood is about 80% (or said adifferent way, there is an 80% confidence score or strength that theAdmin First is missing from the received document 610.

At 710, an indication may be provided of the at least one missing item.Referring also to FIG. 6 for example, the ML model 110 may provide anindication that the Admin First is missing from the received document610. This indication may be an indication that something is missing fromdocument 610, a recommended item for the missing item (e.g., identity ofthe Admin First billing code), and/or a likelihood (strength, score, orconfidence value 80%) regarding the missing item. The indication may beprovided to a user interface or other processor to flag the receiveddocument for further processing, such as correction (adding the missingitem), auditing, etc.

FIG. 8 depicts an example of a system 800 consistent withimplementations of the current subject matter. The computing system 800can be used to implement the user equipment or one or more of thecomponents therein such as the screen share service 405, a screenshotengine configured to take screenshots of the display of the userequipment, and/or other components disclosed herein. As shown in FIG. 8,the computing system 800 can include a processor 810, a memory 820, astorage device 830, and input/output device 840. The processor 810, thememory 820, the storage device 830, and the input/output device 840 canbe interconnected via a system bus 850. The processor 810 is capable ofprocessing instructions for execution within the computing system 800.Such executed instructions can implement one or more components of, forexample, the screen share service 405. In some example embodiments, theprocessor 810 can be a single-threaded processor. Alternately, theprocessor 810 can be a multi-threaded processor. The processor 810 iscapable of processing instructions stored in the memory 820 and/or onthe storage device 830 to display graphical information for a userinterface provided via the input/output device 840.

The memory 820 is a computer readable medium such as volatile ornon-volatile that stores information within the computing system 800.The memory 820 can store data structures representing configurationobject databases, for example. The storage device 830 is capable ofproviding persistent storage for the computing system 800. The storagedevice 830 can be a floppy disk device, a hard disk device, an opticaldisk device, a tape device, a solid-state device, and/or any othersuitable persistent storage mechanisms. The input/output device 840provides input/output operations for the computing system 800. In someexample embodiments, the input/output device 840 includes a keyboardand/or pointing device. In various implementations, the input/outputdevice 840 includes a display unit for displaying graphical userinterfaces. According to some example embodiments, the input/outputdevice 840 can provide input/output operations for a network device. Forexample, the input/output device 840 can include Ethernet ports or othernetworking ports to communicate with one or more wired and/or wirelessnetworks (e.g., a local area network (LAN), a wide area network (WAN),the Internet, the cellular network, and/or the like).

In some example embodiments, the computing system 800 can be used toexecute various interactive computer software applications that can beused for organization, analysis, and/or storage of data in variousformats. Alternatively, the computing system 800 can be used to executeany type of software applications. These applications can be used toperform various functionalities, such as planning functionalities (e.g.,generating, managing, editing of spreadsheet documents, word processingdocuments, and/or any other objects, etc.), computing functionalities,communications functionalities, etc. The applications can includevarious add-in functionalities (e.g., SAP Co-Pilot, SAP IntegratedBusiness Planning as an add-in for a spreadsheet and/or other type ofprogram) or can be standalone computing products and/or functionalities.Upon activation within the applications, the functionalities can be usedto generate the user interface provided via the input/output device 840.The user interface can be generated and presented to a user by thecomputing system 800 (e.g., on a computer screen monitor, etc.).

One or more aspects or features of the subject matter described hereincan be realized in digital electronic circuitry, integrated circuitry,specially designed ASICs, field programmable gate arrays (FPGAs)computer hardware, firmware, software, and/or combinations thereof.These various aspects or features can include implementation in one ormore computer programs that are executable and/or interpretable on aprogrammable system including at least one programmable processor, whichcan be special or general purpose, coupled to receive data andinstructions from, and to transmit data and instructions to, a storagesystem, at least one input device, and at least one output device. Theprogrammable system or computing system may include clients and servers.A client and server are generally remote from each other and typicallyinteract through a communication network. The relationship of client andserver arises by virtue of computer programs running on the respectivecomputers and having a client-server relationship to each other.

These computer programs, which can also be referred to as programs,software, software applications, applications, components, or code,include machine instructions for a programmable processor, and can beimplemented in a high-level procedural and/or object-orientedprogramming language, and/or in assembly/machine language. As usedherein, the term “machine-readable medium” refers to any computerprogram product, apparatus and/or device, such as for example magneticdiscs, optical disks, memory, and Programmable Logic Devices (PLDs),used to provide machine instructions and/or data to a programmableprocessor, including a machine-readable medium that receives machineinstructions as a machine-readable signal. The term “machine-readablesignal” refers to any signal used to provide machine instructions and/ordata to a programmable processor. The machine-readable medium can storesuch machine instructions non-transitorily, such as for example as woulda non-transient solid-state memory or a magnetic hard drive or anyequivalent storage medium. The machine-readable medium can alternativelyor additionally store such machine instructions in a transient manner,such as for example, as would a processor cache or other random accessmemory associated with one or more physical processor cores.

To provide for interaction with a user, one or more aspects or featuresof the subject matter described herein can be implemented on a computerhaving a display device, such as for example a cathode ray tube (CRT) ora liquid crystal display (LCD) or a light emitting diode (LED) monitorfor displaying information to the user and a keyboard and a pointingdevice, such as for example a mouse or a trackball, by which the usermay provide input to the computer. Other kinds of devices can be used toprovide for interaction with a user as well. For example, feedbackprovided to the user can be any form of sensory feedback, such as forexample visual feedback, auditory feedback, or tactile feedback; andinput from the user may be received in any form, including acoustic,speech, or tactile input. Other possible input devices include touchscreens or other touch-sensitive devices such as single or multi-pointresistive or capacitive track pads, voice recognition hardware andsoftware, optical scanners, optical pointers, digital image capturedevices and associated interpretation software, and the like.

In the following, further features and characteristics of the subjectmatter disclosed herein will be described by the following items.

Item 1: A system comprising: at least one data processor; and at leastone memory storing instructions which, when executed by the at least onedata processor, causes the system to at least: determine, for a receiveddocument including at least one item, that the received document likelyincludes at least one missing item, the determination based on at leasta machine learning model and the at least one item; and provide anindication of the at least one missing item.

Item 2: The system of item 1, wherein the received document comprises aninvoice, and wherein the at least one item includes a hospital billingcode.

Item 3: The system of item 1 or 2, wherein the machine-learning modelcomprises a co-occurrence model.

Item 4: The system of item 3, wherein the co-occurrence model comprisesa matrix including a value representative of a likelihood that pairs ofitems are likely to be included in the received document, the pairs ofitems including the at least one item and the at least one missing item.

Item 5: The system of item 3, wherein the co-occurrence model comprisesa co-occurrence matrix including a value representative of a likelihoodthat pairs of items are likely to be included in the received document.

Item 6: The system of any of items 1-5, wherein the system is furthercaused to at least: provide a recommendation item corresponding to theat least one missing item.

Item 7: The system of any of items 1-6, wherein the system is furthercaused to at least: provide a value indicative of a confidence that theat least one missing item is missing from the received document.

Item 8: The system of any of items 1-7, wherein the system is furthercaused to at least: provide a value indicative of a confidence that theat least one missing item is missing from the received document.

Item 9: The system of any of items 1-8, wherein the system is furthercaused to at least: train, based on at least a set of referencedocuments, the machine-learning model, the set of reference documentseach including a set of verified items.

Item 10: The system of any of items 1-9, wherein the machine-learningmodel is generated based on at least a statistical technique, a neuralnetwork, a pattern recognizer, a clustering algorithm, a rule-basedengine, a priori information, a convolutional neural network, and/or arecurrent neural network, wherein the machine-learning model isgenerated based on at least an observed relations table, and/or whereinthe machine learning model is normalized to a percentage value.

Item 11: A method comprising: determining, for a received documentincluding at least one item, that the received document likely includesat least one missing item, the determination based on at least a machinelearning model and the at least one item; and providing an indication ofthe at least one missing item.

Item 12: The method of item 11, wherein the received document comprisesan invoice, and wherein the at least one item includes a hospitalbilling code.

Item 13: The method of items 11 or 12, wherein the machine-learningmodel comprises a co-occurrence model.

Item 14: The method of item 13, wherein the co-occurrence modelcomprises a matrix including a value representative of a likelihood thatpairs of items are likely to be included in the received document, thepairs of items including the at least one item and the at least onemissing item.

Item 15: The method of item 13, wherein the co-occurrence modelcomprises a co-occurrence matrix including a value representative of alikelihood that pairs of items are likely to be included in the receiveddocument.

Item 16: The method of any of items 11-15 further comprising: providinga recommendation item corresponding to the at least one missing itemand/or a value indicative of a confidence that the at least one missingitem is missing from the received document.

Item 17: The method of any of items 11-16 further comprising: training,based on at least a set of reference documents, the machine-learningmodel, the set of reference documents each including a set of verifieditems.

Item 18: The method of any of items 11-17, wherein the machine-learningmodel is generated based on at least a statistical technique, a neuralnetwork, a pattern recognizer, a clustering algorithm, a rule-basedengine, a priori information, a convolutional neural network, and/or arecurrent neural network, wherein the machine-learning model isgenerated based on at least an observed relations table, and/or whereinthe machine learning model is normalized to a percentage value.

Item 19: A non-transitory computer-readable storage medium includingprogram code which when executed causes operations comprising:determining, for a received document including at least one item, thatthe received document likely includes at least one missing item, thedetermination based on at least a machine learning model and the atleast one item; and providing an indication of the at least one missingitem.

Item 20: The non-transitory computer-readable storage medium of item 1919, wherein the received document comprises an invoice, and wherein theat least one item includes a hospital billing code.

In the descriptions above and in the claims, phrases such as “at leastone of” or “one or more of” may occur followed by a conjunctive list ofelements or features. The term “and/or” may also occur in a list of twoor more elements or features. Unless otherwise implicitly or explicitlycontradicted by the context in which it used, such a phrase is intendedto mean any of the listed elements or features individually or any ofthe recited elements or features in combination with any of the otherrecited elements or features. For example, the phrases “at least one ofA and B;” “one or more of A and B;” and “A and/or B” are each intendedto mean “A alone, B alone, or A and B together.” A similarinterpretation is also intended for lists including three or more items.For example, the phrases “at least one of A, B, and C;” “one or more ofA, B, and C;” and “A, B, and/or C” are each intended to mean “A alone, Balone, C alone, A and B together, A and C together, B and C together, orA and B and C together.” Use of the term “based on,” above and in theclaims is intended to mean, “based at least in part on,” such that anunrecited feature or element is also permissible.

The subject matter described herein can be embodied in systems,apparatus, methods, and/or articles depending on the desiredconfiguration. The implementations set forth in the foregoingdescription do not represent all implementations consistent with thesubject matter described herein. Instead, they are merely some examplesconsistent with aspects related to the described subject matter.Although a few variations have been described in detail above, othermodifications or additions are possible. In particular, further featuresand/or variations can be provided in addition to those set forth herein.For example, the implementations described above can be directed tovarious combinations and subcombinations of the disclosed featuresand/or combinations and subcombinations of several further featuresdisclosed above. In addition, the logic flows depicted in theaccompanying figures and/or described herein do not necessarily requirethe particular order shown, or sequential order, to achieve desirableresults. Other implementations may be within the scope of the followingclaims.

What is claimed is:
 1. A system comprising: at least one data processor;and at least one memory storing instructions which, when executed by theat least one data processor, causes the system to at least: determine,for a received document including at least one item, that the receiveddocument likely includes at least one missing item, the determinationbased on at least a machine learning model and the at least one item;and provide an indication of the at least one missing item.
 2. Thesystem of claim 1, wherein the received document comprises an invoice,and wherein the at least one item includes a hospital billing code. 3.The system of claim 1, wherein the machine-learning model comprises aco-occurrence model.
 4. The system of claim 3, wherein the co-occurrencemodel comprises a matrix including a value representative of alikelihood that pairs of items are likely to be included in the receiveddocument, the pairs of items including the at least one item and the atleast one missing item.
 5. The system of claim 3, wherein theco-occurrence model comprises a co-occurrence matrix including a valuerepresentative of a likelihood that pairs of items are likely to beincluded in the received document.
 6. The system of claim 1, wherein thesystem is further caused to at least: provide a recommendation itemcorresponding to the at least one missing item.
 7. The system of claim1, wherein the system is further caused to at least: provide a valueindicative of a confidence that the at least one missing item is missingfrom the received document.
 8. The system of claim 1, wherein the systemis further caused to at least: provide a value indicative of aconfidence that the at least one missing item is missing from thereceived document.
 9. The system of claim 1, wherein the system isfurther caused to at least: train, based on at least a set of referencedocuments, the machine-learning model, the set of reference documentseach including a set of verified items.
 10. The system of claim 1,wherein the machine-learning model is generated based on at least astatistical technique, a neural network, a pattern recognizer, aclustering algorithm, a rule-based engine, a priori information, aconvolutional neural network, and/or a recurrent neural network, whereinthe machine-learning model is generated based on at least an observedrelations table, and/or wherein the machine learning model is normalizedto a percentage value.
 11. A method comprising: determining, for areceived document including at least one item, that the receiveddocument likely includes at least one missing item, the determinationbased on at least a machine learning model and the at least one item;and providing an indication of the at least one missing item.
 12. Themethod of claim 11, wherein the received document comprises an invoice,and wherein the at least one item includes a hospital billing code. 13.The method of claim 11, wherein the machine-learning model comprises aco-occurrence model.
 14. The method of claim 13, wherein theco-occurrence model comprises a matrix including a value representativeof a likelihood that pairs of items are likely to be included in thereceived document, the pairs of items including the at least one itemand the at least one missing item.
 15. The method of claim 13, whereinthe co-occurrence model comprises a co-occurrence matrix including avalue representative of a likelihood that pairs of items are likely tobe included in the received document.
 16. The method of claim 11 furthercomprising: providing a recommendation item corresponding to the atleast one missing item and/or a value indicative of a confidence thatthe at least one missing item is missing from the received document. 17.The method of claim 11 further comprising: training, based on at least aset of reference documents, the machine-learning model, the set ofreference documents each including a set of verified items.
 18. Themethod of claim 11, wherein the machine-learning model is generatedbased on at least a statistical technique, a neural network, a patternrecognizer, a clustering algorithm, a rule-based engine, a prioriinformation, a convolutional neural network, and/or a recurrent neuralnetwork wherein the machine-learning model is generated based on atleast an observed relations table, and/or wherein the machine learningmodel is normalized to a percentage value.
 19. A non-transitorycomputer-readable storage medium including program code which whenexecuted causes operations comprising: determining, for a receiveddocument including at least one item, that the received document likelyincludes at least one missing item, the determination based on at leasta machine learning model and the at least one item; and providing anindication of the at least one missing item.
 20. The non-transitorycomputer-readable storage medium of claim 19, wherein the receiveddocument comprises an invoice, and wherein the at least one itemincludes a hospital billing code.